VIPAS: Virtual Link Powered Authority Search in the Web

mclab

December 2, 2003

Speaker : Ming-Syan Chen

National Taiwan University

Reporter : Chen-Yu Yang

1 Instroduction

The point of this speech are the search engine on web and study user behavior to derive a method for search . Add an user behavior observation to calculate the weight by counting click rate . Use the asp to calculate the click count and redirect the user to what they want to go .

2 Essentials

This research want to find the most relevant pages satisfying the user’s information need in the web . Traditional method is keyword-based search which may not find the relevant pages because the page does not contain the keywords . So they analyze the links contained in the web pages instead of ranking by keyword . Authority pages which pointed to by many other pages and hub pages which pointing to many other pages are proposed . An authority pointed to by many hub pages is an even better authority and a hub pointing to many authority pages is an even better hub . Based on this argument, the goal of HITS is to find the set of best authority pages. But hit have the problem that sometimes a link will not be put in the page even though its destination is very relevant . Links in Web pages only reflect page creators judgment. So the author think that the page reader’s consideration should be of equal importance . Then they propose the virtual links . The basic idea is to identify pages that are heavily accessed within a period, and form a hot set from these pages . Create virtual links for pages in the hot set and incorporate them into the computation of authority scores. In Virtual LInk Powered Authority Search(VIPAS) , there are three phase .

• Initialization Phase

– For a query term, perform the regular HITS analysis

– Collect a base set of pages with computed authority and hub scores and store them in the database

• Virtual Link Collection Phase

– Monitor the user behavior to see whether a URL in the list is clicked by the user or not

– After a period of user behavior observation, put URLs that are often accessed into the hot set

– Create virtual links for pages in the hot set

• Refinement Phase

– For each page in the hot set, compute its new authority and hub scores

– Run several iterations of score updating for pages in the base set

In an observing period, pay attention to clicks of continuous URLs in the list . When a user continuously clicks several URLs and then skips some URLs following, mark those that have been skipped . Exclude pages marked with a frequency greater than the one from the forming of hot sets . Among pages left, those that are accessed by at least severalusers are put into the hot set Discrepancy coefficient is proposaled for calculating the position of real result from hot set . Calculating the grouping coefficient for stability . Grouping coefficient is the standard deviation of each algorithm’s discrepancy coefficients for all keywords . The lower the grouping coefficient , the more stable the algorithm is .

3 Conclusion

The search result is highly related to the user behavior . Under the normal situation , if user does not mean to trick , the result will be almost the best . If user use some trick , for example , reload many times to add the hits , the result may be wrong . The transliteration between different languages is also a problem . They use simulation to prove their result . So it needs more provision expect for simulation . They use simple tools such as asp and database and implement their idea , to do more than what database can do . So the one man’s idea is very important . It may needn’t complicated tools . But the kernal mind is someone cann’t image . Originality is thinking different and do something specially .

4 Reference

1. http://ftp.informatik.uni-trier.de/ ley/db/indices/a-tree/c/Chen:Ming=Syan.html 2. http://www.informatik.uni-trier.de/ ley/db/indices/a-tree/l/Lin:Chi=Chun.html 3. http://www2.ee.ntu.edu.tw/ mschen/